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Approximate computing is being considered as a promising design paradigm toovercome the energy and performance challenges in computationally demandingapplications. If the case where the accuracy can be configured, the qualitylevel versus energy efficiency or delay also may be traded-off. For thistechnique to be used, one needs to make sure a satisfactory user experience.This requires employing error predictors to detect unacceptable approximationerrors. In this work, we propose a scheduling-aware feature selection methodwhich leverages the intermediate results of the hardware accelerator to improvethe prediction accuracy. Additionally, it configures the error predictorsaccording to the energy consumption and latency of the system. The approachenjoys the flexibility of the prediction time for a higher accuracy. Theresults on various benchmarks demonstrate significant improvements in theprediction accuracy compared to the prior works which used only the acceleratorinputs for the prediction.

Space Expansion of Feature Selection for Designing more Accurate Error Predictors